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Data Analysis and Knowledge Discovery  0, Vol. Issue (): 1-    DOI: 10.11925/infotech.2020.0364
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Using Hierarchical Attention Network Model to Recognize Structure Functions of Academic Articles
Chenglei Qin,Chengzhi Zhang
Department of Information Management, School of Economics and Management, Nanjing University of Science and Technology, Nanjing 210094
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Abstract  

[Objective]The goal of the functional recognition of academic text structure is to automatically recognize the function of the academic text section. [Methods]We construct different-grained hierarchical attention network model and use multiple deep learning models as encoder to automatically identify the function of academic text structure. In addition, the effect of the traditional machine learning models with different text feature vectors and Bert model in the functional recognition of academic text structure are analyzed. And then, we used the distribution similarity of the references, and the similarity of cue word distribution to evaluate the effect of the model in real data. The domain adaptability of the hierarchical attention network model is also analyzed. [Results]The hierarchical attention network model at the sentence level with Bi-Lstm+Attention as the encoder outperforms other methods,the value of Macro-F1 is 0.8661; Secondly, the performance of model classification has dropped significantly in the fields with great differences, Macro-F1 has a minimum value of 0.4554. [Limitations] The function of section with mixed structure can not be recognized, and the logical relationship in article structures is not used in the HAN model. [Conclusions] Sentence level HAN model can better recognize the structure function, and incorporating of academic text structure information can enrich and expand the research content and scope based on the whole text of academic papers

Key words Function recognition of academic text structure      Hierarchical attention network      IMRaD;Domain adaptability analysis      
Published: 03 August 2020
ZTFLH:  TP393,G250  

Cite this article:

Chenglei Qin, Chengzhi Zhang. Using Hierarchical Attention Network Model to Recognize Structure Functions of Academic Articles . Data Analysis and Knowledge Discovery, 0, (): 1-.

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http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2020.0364     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y0/V/I/1

[1] Qin Chenglei,Zhang Chengzhi. Recognizing Structure Functions of Academic Articles with Hierarchical Attention Network[J]. 数据分析与知识发现, 2020, 4(11): 26-42.
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